Automated workflow composition in mass spectrometry-based proteomics.
Journal
Bioinformatics (Oxford, England)
ISSN: 1367-4811
Titre abrégé: Bioinformatics
Pays: England
ID NLM: 9808944
Informations de publication
Date de publication:
15 02 2019
15 02 2019
Historique:
received:
29
01
2018
revised:
06
07
2018
accepted:
26
07
2018
pubmed:
31
7
2018
medline:
5
11
2019
entrez:
31
7
2018
Statut:
ppublish
Résumé
Numerous software utilities operating on mass spectrometry (MS) data are described in the literature and provide specific operations as building blocks for the assembly of on-purpose workflows. Working out which tools and combinations are applicable or optimal in practice is often hard. Thus researchers face difficulties in selecting practical and effective data analysis pipelines for a specific experimental design. We provide a toolkit to support researchers in identifying, comparing and benchmarking multiple workflows from individual bioinformatics tools. Automated workflow composition is enabled by the tools' semantic annotation in terms of the EDAM ontology. To demonstrate the practical use of our framework, we created and evaluated a number of logically and semantically equivalent workflows for four use cases representing frequent tasks in MS-based proteomics. Indeed we found that the results computed by the workflows could vary considerably, emphasizing the benefits of a framework that facilitates their systematic exploration. The project files and workflows are available from https://github.com/bio-tools/biotoolsCompose/tree/master/Automatic-Workflow-Composition. Supplementary data are available at Bioinformatics online.
Identifiants
pubmed: 30060113
pii: 5060940
doi: 10.1093/bioinformatics/bty646
pmc: PMC6378944
doi:
Types de publication
Journal Article
Research Support, Non-U.S. Gov't
Langues
eng
Sous-ensembles de citation
IM
Pagination
656-664Informations de copyright
© The Author(s) 2018. Published by Oxford University Press.
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